Inventory Planning Blog

No matter how small the inventory, without some idea of the demand planning roadmap, companies will find themselves in a ditch with either too much or too little inventory. This is too expensive an investment for a company of any size to ignore.

Investing in Demand Planning Software is the first of two critical steps in keeping your inventory on the road, and not in the ditch. Imagine a forecast as the yellow line down the middle of a road. Sometimes the traffic (i.e. demand) will be on the left side of the line, sometimes it will be on right. When you do not have enough inventory to meet demand you will be off the road in the left ditch. When you are carrying too much, you will also be off the road in the right ditch. The object is to stay somewhere to the right of the line – just like driving.

Where to be to the right of the line is the role of inventory optimization, the second critical component to the inventory roadmap. Whether you are optimizing on service level, cost and or space, there is some quantity in the right lane that will work best for you based on your business criteria and budget. So just having a forecast is only the first step in good inventory planning management.

The combination of a good forecast and a good inventory optimization method are key to making the most of your inventory investment. You do not have to be a statistician to benefit from the power of advanced planning – the system does it all for you!

Demand PlanningDemand Planning is exactly what it says - planning demand. Two sub-sets of demand planning are: Forecasting and Planned Demand (i.e. equipment maintenance organizations that schedule item usage in advance). The most common inventory forecasting algorithms are statistical models.

Most forecast systems are labor intensive requiring several highly skilled people to analyze forecast models, select the best forecast method for each item, tweak the results, and constantly monitor forecast accuracy – always with less than perfect results. In addition, they usually require fairly long processing times. This just does not work in most businesses, nor is it necessary.

Forecasting MethodsThe first forecasting step: For each item being managed decide which forecast model works best. The second forecasting step: Apply a forecast model. Some advanced inventory planning solutions do both steps automatically and quickly.

Analytical AlgorithmUsing history to predict the future is the most common type of forecast modeling. Such forecast methods are called historical time series models. Extensive analysis of historic time series has shown that most demand histories have distinctive patterns consisting of one or more characteristic e.g. seasonality, trend, random-intermittent demand, highly stable, highly erratic (which statisticians would tend to describe as lots of ‘noise’), batch order history (e.g. master distributors), single order history (e.g. service parts), etc. Based on the outcome, the system automatically applies the best model for forecasting demand.

Applying the best forecast model is not only fast, from a processing standpoint, it requires no user intervention unless one wants to make modifications to the outcome. The value of this automated method enables users to plan faster and more accurately, and allows them to focus on exceptions and processing orders, rather than tediously laboring over selecting or evaluating the best forecast algorithm for each and every item or item/location combination.

Historical Time Series Based ModelsHistoric data are an excellent source for computing a forecast, particularly for finished goods and service parts. Whether applying all or part of the time series to compute a forecast, oftentimes, it is the only possible basis of providing a forward look at future requirements.

Trend and Seasonal ModelsThese models are modern statistical algorithms, which automatically detect the existence and characteristics of the components of a time series. They assume any time series to have the following components: trend, seasonality and noise. These models decompose the components of the time series and then extrapolate future values according to the characteristics of these components. The system measures the trend and seasonal components to determine the strength of each. Keep in mind, the existence of seasonality, requires at least 24 or more periods of data to be confirmed. Once the strength of the trend and seasonal components are determined, the appropriate model is applied.